Exploring Public Relations Research Topics and Inter-Cluster Dynamics Through Computational Modeling (2010-2020): A Study Based on Two SSCI Journals

Alvin Zhou, Luke W. Capizzo, Tyler G. Page, Elizabeth L. Toth

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This project addresses the evolution of public relations research over the past decade by examining its two SSCI-indexed journals with methods that can reveal the influence of multiple categories of research clusters. Modeling the full text of all 1,293 published articles in Public Relations Review (PRR) and the Journal of Public Relations Research (JPRR) from 2010 to 2020 (7,400,685 words), we identified nine non-discrete clusters in public relations research. Using three computational methods–structural topic modeling, inter-cluster network analysis, and network simulation–we found that (1) the strategic management cluster emerged as the most central for the past decade, followed by public relations professionalism, digital media, crisis communication, internal communication, global public relations, rhetoric and philosophy, media relations, and critical studies, ranked by their proportions in the scholarship; (2) JPRR had greater emphasis on the strategic management cluster relative to PRR, which offered a more diverse representation; (3) little longitudinal change occurred throughout the decade, although internal communication gained traction and public relations professionalism and media relations lost ground as the decade progressed; and 4) the last ten years of public relations research did not see intersection among theoretical traditions from different clusters as much as expected, leaving opportunity for more inter-cluster knowledge production. Theoretical and practical implications for the public relations research community are discussed.

Original languageEnglish (US)
Pages (from-to)135-161
Number of pages27
JournalJournal of Public Relations Research
Issue number3
StatePublished - 2023

Bibliographical note

Funding Information:
The authors would like to thank Hyunjin Song for his invaluable advice at the early stage of the project. The authors are also grateful to editors, anonymous reviewers, Kokil Jaidka, and Tian Yang for their insightful comments. All remaining errors are our own. Data and scripts have been deposited onto https://doi.org/10.17605/OSF.IO/25DSB for replication purposes.

Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.


  • Bipartite Network Analysis
  • computational methods
  • network simulation
  • public relations
  • research review
  • structural topic modeling


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